code: def _parse_loop_limits(
      min_steps: Optional[int],
      max_steps: int,
      min_epochs: Optional[int],
      max_epochs: Optional[int],
      trainer: "pl.Trainer",
  ) -> Tuple[int, int]:
      """This utility computes the default values for the minimum and maximum number of steps and epochs given the values
      the user has selected.
  
      Args:
          min_steps: Minimum number of steps.
          max_steps: Maximum number of steps.
          min_epochs: Minimum number of epochs.
          max_epochs: Maximum number of epochs.
          trainer: Trainer instance.
  
      Returns:
          The parsed limits, with default values being set for the ones that the user did not specify.
  
      """
      if max_epochs is None:
          if max_steps == -1 and not any(
              isinstance(cb, Timer) for cb in trainer.callbacks
          ):
              rank_zero_warn(
                  "`max_epochs` was not set. Setting it to 1000 epochs. To train without an epoch limit,"
                  " set `max_epochs=-1`.",
                  category=PossibleUserWarning,
              )
              max_epochs = 1000
          else:
              max_epochs = -1
  
      if min_epochs is None and min_steps is not None:
          # setting this allows FitLoop.done to re-evaluate should_stop when it gets triggered `on_fit_start`
          min_epochs = 1
  
      if min_epochs is None:
          # the default value is 0 so no training will be done when should_stop is triggered `on_fit_start`
          min_epochs = 0
  
      return min_epochs, max_epochs
  
imports: import inspect
  from contextlib import contextmanager
  from typing import Any, Callable, ContextManager, Generator, Optional, Tuple, Type
  import torch
  import torch.distributed
  from torch import Tensor
  import lightning.pytorch
  from lightning.fabric.utilities.distributed import _distributed_is_initialized
  from lightning.fabric.utilities.imports import _TORCH_EQUAL_2_0, _TORCH_GREATER_EQUAL_1_13
  from lightning.fabric.utilities.warnings import PossibleUserWarning
  from lightning.pytorch.accelerators.xla import XLAAccelerator
  from lightning.pytorch.callbacks.timer import Timer
  from lightning.pytorch.loops import _Loop
  from lightning.pytorch.loops.fetchers import _DataFetcher, _DataLoaderIterDataFetcher, _PrefetchDataFetcher
  from lightning.pytorch.loops.progress import _BaseProgress
  from lightning.pytorch.strategies import FSDPStrategy
  from lightning.pytorch.strategies.parallel import ParallelStrategy
  from lightning.pytorch.strategies.strategy import Strategy
  from lightning.pytorch.trainer.states import RunningStage
  from lightning.pytorch.utilities.rank_zero import rank_zero_warn
  from lightning.pytorch.utilities.signature_utils import is_param_in_hook_signature
label_desc: Use image node_id 2 for calling a global function with example usage: _parse_loop_limits(min_steps, max_steps, min_epochs, max_epochs, trainer) and returns: min_epochs,  max_epochs